Jakob Sauer Jørgensen

Jakob Sauer Jørgensen serves as a Senior Researcher in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), affiliated with the UltraSound and Biomechanics Scientific Computing group within the Center for Fast Ultrasound Imaging. His work spans computational mathematics, medical imaging, and uncertainty quantification.

His research focuses on Image Reconstruction, Tomographic Reconstruction, and Uncertainty Quantification with emphasis on regularization techniques, sparsity, and Bayesian approaches. Key methodologies include total variation, inverse problems, and computational frameworks like CUQIpy for Python-based uncertainty quantification in inverse problems.

His publication trends show strong emphasis on computational methods for medical imaging (2025), uncertainty quantification frameworks (2024-2025), and applications in nuclear fusion diagnostics (2024). Recent work integrates neural networks with traditional reconstruction methods and develops open-source tools for uncertainty modeling.

He actively supervises PhD students across multiple projects including neural network-based X-ray tissue imaging, Mars meteorite analysis, and additive manufacturing modeling.

Current projects include:

• Neural networks-based X-ray tissue imaging for robotic automation (2025-2028)
• Multi-modality reconstruction for Mars meteorite analysis (2024-2027)
• Modeling of volumetric additive manufacturing (2023-2026)
• Uncertainty quantification for tomographic reconstruction (2020-2024)